AI & DATA

RAG quality starts with a knowledge and access contract

Retrieval quality depends on source ownership, freshness, chunking, permissions, evaluation and citation behavior.

8 min guide

Define the decision boundary

State what the system may recommend, decide, change or escalate.

Document sources, freshness, permissions, lineage and quality expectations.

Measure unsupported outputs, edge cases, latency, cost and handoff quality.

Models, prompts, tools, data schemas and policies should change under controlled versions.

Monitoring and review must connect technical quality to real business outcomes.

Ground generative AI in controlled enterprise knowledge with retrieval, citation and access policies.

Build ingestion, transformation, feature, quality and lineage foundations for AI systems.

Data platforms, AI products, model operations, analytics and governed automation.

Product delivery, cloud platforms, managed IT, security and technical operations.

Product engineering, subscriptions, customer operations, cloud delivery and scale.

Patient or member service workflows, secure systems, operational support and data integration.

Secure digital products, customer operations, data workflows, automation and technology controls.